IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v50y2023i4p984-1016.html
   My bibliography  Save this article

Estimating the parameters of a dependent model and applying it to environmental data set

Author

Listed:
  • V. Mohtashami-Borzadaran
  • M. Amini
  • J. Ahmadi

Abstract

In this paper, a new dependent model is introduced. The model is motivated using the structure of series-parallel systems consisting of two series-parallel systems with a random number of parallel sub-systems that have fixed components connected in series. The dependence properties of the proposed model are studied. Two estimation methods, namely the moment method, and the maximum likelihood method are applied to estimate the parameters of the distributions of the components based on observing the system's lifetime data. A Monte Carlo simulation study is used to evaluate the performance of the estimators. Two real data sets are used to illustrate the proposed method. The results are useful for researchers and practitioners interested in analyzing bivariate data related to extreme events.

Suggested Citation

  • V. Mohtashami-Borzadaran & M. Amini & J. Ahmadi, 2023. "Estimating the parameters of a dependent model and applying it to environmental data set," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(4), pages 984-1016, March.
    • Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:984-1016
    DOI: 10.1080/02664763.2021.2006613
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2021.2006613
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2021.2006613?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:984-1016. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.